Mutation landscape of normal karyotype myelodysplastic syndromes and their prognostic impact
Authors: Wei Wang, Yudi Zhang, Wenli Yang, Yueyuan Han, Lingxu Jiang, Xiaozhen Liu, Wei Lang, Yingwan Luo, Shuanghong Zhu, Xinping Zhou, Lu Wang, Li Ye, Liya Ma, Hongyan Tong
Journal: American Journal of Hematology (2023)

Myelodysplastic syndrome (MDS) patients exhibit significant heterogeneity in clinical manifestations and prognoses.1 Accurate estimation of the survival probabilities of MDS is important in clinical decision-making.2, 3 Recent advancements in genomic testing technology have introduced a new era of MDS prognostic models. Incorporating molecular factors into conventional prognostic scoring system has proven to be an effective strategy for improving the predictive performance.4, 5 However, another issue worth noting is that those models were often constructed based on either all MDS patients or subgroups categorized based on the Revised International Prognostic Scoring System (IPSS-R). The cytogenetic background is often disregarded when confirming the prognostic importance of genes. For instance, the Molecular IPSS (IPSS-M)5 and the models developed by Nazha et al.4 include “TP53 mutations” as a significant negative prognostic factor. However, TP53 mutated more prevalent in MDS patients with complex karyotypes (60%–80%), whereas the frequency is lower in patients with a normal karyotype (NK).6-8 Although TP53 mutations are strongly associated with poor prognosis in MDS patients with complex or monosomal karyotypes, it had no prognostic value in MDS patients with normal karyotypes according to some reports.9 Therefore, the prognostic mutation spectrum of the entire MDS population may not be applicable to patients with normal karyotypes. Thus, we investigated the frequency of gene mutations and their impact on prognosis in de novo NK MDS patients. 237 de novo NK MDS patients at the First Affiliated Hospital of Zhejiang University were enrolled in the training set; MDS was diagnosed based on the WHO 2016 criteria.10 The Ethics Committee of the First Affiliated Hospital approved the study protocol, and informed consent was obtained from all participants. The study was conducted in accordance with the Declaration of Helsinki. All patients underwent pre-treatment examinations, including next-generation sequencing (NGS), bone marrow (BM) examination, and cytogenetic and hematological analysis. To ensure diagnostic accuracy, at least 20 metaphase spreads were used to determine the presence of NK in MDS using G-banding or R-banding analysis.11, 12 Targeted NGS was performed on individual BM cells of all patients using a predefined gene list, including SETBP1, U2AF1, ASXL1, CBL, CEBPA, CSF3R, DNMT3A, ETV6, EZH2, IDH1, IDH2, KRAS, NPM1, NRAS, PHF6, PTPN11, RUNX1, SF3B1, SRSF2, TP53, WT1, ZRSR2, TET2, and FLT3. Details about gene sequencing and mutation analyses are described in Supplemental methods. NK primary MDS patients from the International Working Group for the Prognosis of MDS (IWG-PM) cohort of the IPSS-M study were used as the external validation cohort, which was obtained from the cBioPortal platform (http://www.cbioportal.org/datasets). Comparison between categorical and continuous co-variates was made by Chi-square and Mann–Whitney U tests. We used natural logarithm transformation to achieve a normal distribution for the highly skewed platelet (PLT) count data; the other hematological data required no adjustments. Follow-up data were available for 100% of subjects in our cohort. The last follow-up was on February 3, 2023, with a median follow-up of 1.81 years (interquartile range [IQR], 0.93 to 3.46 years). Overall survival (OS) is the length of time from the date of diagnosis until death or the last follow-up. Leukemia-free survival (LFS) is the length of time from diagnosis until leukemia transformation or death from any cause. Survival rates were assessed with Kaplan–Meier curves, and their significance was determined using the log-rank test. Additionally, both univariate and multivariate Cox regression analyses were performed to confirm the relationship between features and prognosis. We simplified the p-values for some results, where “ns” indicates not significant and *, **, ***, and **** indicate p-values of <.05, <.01, <.001, and <.0001, respectively. Statistical analyses were conducted, and figures were created using R software (version 4.2.1; R Development Core Team, Vienna, Austria). Baseline co-variates of 237 de novo NK MDS patients in our cohort and 691 de novo NK MDS patients from IWG-PM cohort are displayed in Figure 1A. Both cohorts had a male predominance. Patients in the training cohort were younger (62 vs. 73 years, p < .001) and had higher BM blast percentages (3.5 vs. 3.0, p = .04), lower hemoglobin (8.1 g/dL vs. 9.9 g/dL, p < .001), PLT (66 × 109/L vs. 138 × 109/L, p < .001), and absolute neutrophil count (ANC) (1.2 × 109/L vs. 2 × 109/L, p < .001). Consistent with these findings, training cohort had lower IPSS-R (p < .001) and IPSS-M (p < .001) risks. According to the data of treatment, our center has a higher proportion of patients receiving disease-modifying treatment regimens compared to the IWG-PM cohort. There were no significant differences in terms of median follow-up time (p = .124) and the incidence rate of LFS events (p = .316) between the two cohorts. 24 genes were analyzed in our cohort, and the top 10 genes with the highest frequencies of mutations included ASXL1 (22%), TET2 (20%), RUNX1 (13%), SF3B1 (13%), U2AF1 (11%), DNMT3A (11%), EZH2 (7%), ZRSR2 (5%), SRSF2 (5%), and CEBPA (4%) (Figure 1B). The 10 most frequent mutated genes in IWG-PM cohort were TET2 (36%), SF3B1 (30%), ASXL1 (26%), SRSF2 (18%), DNMT3A (18%), RUNX1 (13%), U2AF1 (7%), ZRSR2 (7%), IDH2 (5%), and IDH1 (4%) (Figure 1B). The features of mutation topography were similar to the entire MDS population according to previous studies.13-15 Notably, TP53 mutation frequency did not rank among the top 10 genes in the training or IWG-PM cohort, which disagrees with previous studies that did not differentiate MDS by karyotype (Figure 1B).13-15 In addition, survival analysis showed that TP53 mutation was not associated with prognosis in the training (p = .11) or IWG-PM (p = .7) cohort (Figures S1 and S2). Median OS time of NK MDS subjects was 7.38 years (LFS time was 6.14 years) in our cohort and was 4.99 years (LFS time was 4.48 years) in IWG-PM cohort (Figure S3A,B). Univariate Cox regression analysis revealed that In(PLT) and hemoglobin (HB) were protective factors (hazard ratio [HR]: 0.77, 95% confidence interval [CI]: 0.62–0.96, p = .02 and HR: 0.83, 95% CI: 0.76–0.92, p < .001, respectively). Conversely, age ≥ 60 years (HR: 2.89, 95% CI: 1.73–4.85, p < .001); higher BM blast (HR: 1.12, 95% CI: 1.07–1.17, p < .001); and eight mutations (CEBPA [HR: 4.97, 95% CI: 2.45–10.1, p < .001], EZH2 [HR: 2.4, 95% CI: 1.19–4.84, p = .014], NPM1 [HR: 3.11, 95% CI: 1.25–7.72, p = .015], NRAS [HR: 3.09, 95% CI: 1.24–7.68, p = .015], RUNX1 [HR: 3.05, 95% CI: 1.77–5.25, p < .001], U2AF1 [HR: 3.53, 95% CI: 2.03–6.13, p < .001], FLT3 [HR: 7.16, 95% CI: 1.73–29.68, p = .0066], and SRSF2 [HR: 2.36, 95% CI: 1.02–5.47, p = .046]) had a significant negative prognostic impacts (Figure 1C). Notably, the prognostic significance of these 12 features was consistent in the IWG-PM cohort, except for NPM1 (Figure 1C). The low mutation frequency of NPM1 may account for this difference. When considering LFS as the outcome, the majority of these 11 features maintained significance in both cohorts (Figure S3C). Interestingly, the ANC was not significantly associated with OS and LFS (p = .117, and p = .608, respectively), which is consistent in IWG-PM cohort (p = .58, and p = .21, respectively) (Tables S1 and S2). Significant features identified through univariate Cox regression in both cohorts were used as input variables for multivariate Cox regression analysis. When considering OS as the endpoint, age ≥ 60 years (HR: 2.6, 95% CI: 1.46–4.62, p = .0011 and HR: 3.45, 95% CI: 1.52–3.61, p < .001, respectively), HB (HR: 0.86, 95% CI: 0.78–0.96, p = .0057 and HR: 0.77, 95% CI: 0.72–0.83, p < .001, respectively), In(PLT) (HR: 0.77, 95% CI: 0.6–0.99, p = .043 and HR: 0.85, 95% CI: 0.73–0.98, p = .023, respectively), higher BM blast (HR: 1.09, 95% CI: 1.04–1.15, p < .001 and HR: 1.05, 95% CI: 1.02–1.07, p < .001, respectively), FLT3 (HR: 7.82, 95% CI: 1.63–37.61, p = .01 and HR: 4.01, 95% CI: 1.62–9.92, p = .0026, respectively), and CEBPA (HR: 2.66, 95% CI: 1.14–6.18, p = .023 and HR: 2.15, 95% CI: 1.23–3.77, p = .0076, respectively) demonstrated independent prognostic significance in the training and IWG-PM cohorts. U2AF1 (HR: 3.06, 95% CI: 1.6–5.84, p < .001) was independently associated with worse OS in the training cohort, and EZH2 (HR: 1.79, 95% CI: 1.07–2.98, p = .026), RUNX1 (HR: 1.9, 95% CI: 1.36–2.67, p < .001), and SRSF2 (HR: 1.46, 95% CI: 1.05–2.02, p = .026) were independently associated with worse OS in the IWG-PM cohort (Figure 1D, Table S1). When considering LFS as the endpoint, age ≥ 60 years (HR: 2.22, 95% CI: 1.34–3.69, p = .002 and HR: 3.15, 95% CI: 2.02–4.9, p < .001, respectively), HB (HR: 0.9, 95% CI: 0.83–0.99, p = .022 and HR: 0.83, 95% CI: 0.77–0.88, p < .001, respectively), In(PLT) (HR: 0.79, 95% CI: 0.63–0.99, p = .042 and HR: 0.85, 95% CI: 0.73–0.98, p = .023, respectively), and higher BM blast (HR: 1.12, 95% CI: 1.08–1.17, p < .001 and HR: 1.07, 95% CI: 1.05–1.1, p < .001, respectively) demonstrated independent prognostic significance in both cohorts. In the training cohort, U2AF1 (HR: 2.07, 95% CI: 1.17–3.67, p = .013) was still an independent adverse variable, and CEBPA (HR: 2.11, 95% CI: 0.99–4.5, p = .054) tends to be associated with worse LFS. RUNX1 (HR: 2.04, 95% CI: 1.48–2.82, p < .001) and FLT3 (HR: 4.24, 95% CI: 1.56–11.5, p = .0047) were independently associated with worse LFS in the IWG-PM cohort (Figure S3D, Table S2). As anticipated, TP53 mutation was also not significantly associated with OS and LFS (p = .122, and p = .111, respectively), which suggests that gene mutations may have different contributions to the disease in different karyotypic backgrounds (Tables S1 and S2). Remarkably, TP53 in NK MDS shows unique traits with low mutation frequency, modest VAF, and rare multi-hit mutations (Figure S4). In conclusion, our study examined the mutation landscape and prognostic implications in NK MDS across both our cohort and the IWG-PM cohort. Our findings highlight the importance of considering karyotypic backgrounds when evaluating the prognostic relevance of specific gene mutations in MDS. Hongyan Tong, Wei Wang, and Yudi Zhang conceptualized and designed the study. Wei Wang performed the statistical analysis. Wei Wang, Yudi Zhang, and Wenli Yang wrote the paper. Yueyuan Han, Wenli Yang, Lingxu Jiang, Xiaozhen Liu, and Wei Lang provided patient samples and clinical data. Yingwan Luo, Shuanghong Zhu, Xinping Zhou, Lu Wang, Li Ye, and Liya Ma collected the mutational data. Hongyan Tong supervised the study and revised the manuscript. We sincerely appreciate the selfless sharing of clinical and mutation data from the IPSS-M study by the International Working Group for Prognosis in MDS (IWG-PM). This work was supported by grants from the National Natural Science Foundation of China (Grant Nos. 81970117 and 82270146). The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The clinical dataset used during the current study is available from the corresponding author on reasonable request. Table S1. Univariate and multivariate Cox regression analyses in two cohorts (OS). Table S2. Univariate and multivariate Cox regression analyses in two cohorts (LFS). Figure S1. Kaplan–Meier survival curves for 24 genes in our center's cohort (training cohort) were individually constructed, using overall survival (OS) as the endpoint. Mutational status was delineated by color, with red denoting mutations and blue indicating wild type. Figure S2. Kaplan–Meier survival curves for 24 genes in IWG-PM cohort were individually constructed, using OS as the endpoint. Mutational status was delineated by color, with red denoting mutations and blue indicating wild type. Figure S3. (A) Kaplan–Meier survival curves for two cohorts (with OS as the endpoint). (B) Kaplan–Meier survival curves for two cohorts (with leukemia-free survival [LFS] as the endpoint). (C and D) Forest plots for univariate and multivariate Cox regression analyses (with LFS as the endpoint), employing colors to distinguish between different cohorts and various shapes to represent distinct p-values. Figure S4. TP53 mutation status in NK MDS. (A) Lollipop chart illustrating TP53 mutation sites within the training cohort. (B) Lollipop chart illustrating TP53 mutation sites within the IWG-PM cohort. (C) Specific information on TP53 mutations in both cohorts. (D) Violin plots illustrating the variant allele frequency (VAF) of TP53 mutations in both cohorts. (E) Presentation of multi-hit occurrences of TP53 mutations. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. 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